Pattern Classification, Part 1This unique text/professional reference provides the information you need to choose the most appropriate method for a given class of problems, presenting an in-depth, systematic account of the major topics in pattern recognition today. A new edition of a classic work that helped define the field for over a quarter century, this practical book updates and expands the original work, focusing on pattern classification and the immense progress it has experienced in recent years."--BOOK JACKET. |
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Page 285
... units . Cleary , such a network is an extension of the two - layer networks we studied in Chapter 5. The function of ... output units will be the values of the discriminant functions used for classification . We can clarify our notation ...
... units . Cleary , such a network is an extension of the two - layer networks we studied in Chapter 5. The function of ... output units will be the values of the discriminant functions used for classification . We can clarify our notation ...
Page 286
... output z can also be thought of as a function of the input feature vector x . When there are c output units , we can ... output unit and label a pattern by the sign of the output z . = = It is easy to verify that the three - layer ...
... output z can also be thought of as a function of the input feature vector x . When there are c output units , we can ... output unit and label a pattern by the sign of the output z . = = It is easy to verify that the three - layer ...
Page 360
... units and other units as output units . This is illustrated in Fig . 7.7 , where the input units accept binary feature information and the output units represent the output categories , generally in the familiar 1 - of- c representation ...
... units and other units as output units . This is illustrated in Fig . 7.7 , where the input units accept binary feature information and the output units represent the output categories , generally in the familiar 1 - of- c representation ...
Contents
MAXIMUMLIKELIHOOD AND BAYESIAN | 84 |
NONPARAMETRIC TECHNIQUES | 161 |
LINEAR DISCRIMINANT FUNCTIONS | 215 |
Copyright | |
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Other editions - View all
Computer Manual in MATLAB to accompany Pattern Classification David G. Stork,Elad Yom-Tov No preview available - 2004 |
Computer Manual in MATLAB to accompany Pattern Classification David G. Stork,Elad Yom-Tov No preview available - 2004 |
Common terms and phrases
analysis approach assume backpropagation Bayes Bayesian bias binary Boltzmann calculate Chapter cluster centers component classifiers Consider convergence corresponding covariance matrix criterion function d-dimensional data set decision boundary denote derivation discriminant function distance distribution entropy error rate feature space FIGURE Gaussian given gradient descent Hidden Markov Models hidden units independent input iteration jackknife estimate labeled large number learning algorithm maximum-likelihood estimate mean methods minimize minimum minimum description length mixture density nearest-neighbor neural networks node nonlinear normal number of clusters number of samples obtain optimal output units p(xw parameters pattern recognition Perceptron points prior probabilities probability density problem procedure random variables randomly Section sequence shown shows simple solution split statistical statistically independent string Suppose target tion training data training error training patterns training set tree two-category unsupervised learning variance w₁ weight vector x₁ zero